Method and system for creating a predictive model for targeting web-page to a surfer

• US 8,762,313 B2
• Filed: 06/10/2011
• Issued: 06/24/2014
• Est. Priority Date: 07/25/2008
• Status: Active Grant

• Current Assignee: Amedesa, Ltd., LivePerson Incorporated
• Original Assignee: LivePerson Incorporated
• Inventors: Lahav, Shlomo, Ron, Ofer
• Primary Examiner(s): Gaffin, Jeffrey A
• Assistant Examiner(s): Kim, David H
• Application Number:
  US13/157,936
• Publication Number:
  US 20110246406A1
• Time in Patent Office: 1,110 Days
• Field of Search: None
• US Class Current: 706/46
• CPC Class Codes: G06F 30/20 Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/046 Forward inferencing; Production systems
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

A system and a method for creating a predictive model to select an object from a group of objects that can be associated with a requested web-page, wherein a configuration of the requested web-page defines a subgroup of one or more selected objects from the group of objects. Each web-page can include one or more links to be associated with content objects from the group. For each content object presented over a requested web-page, one or more predictive model with relevant predictive factors is processed such that the predicted objective, the probability of success for example, is calculated. A success is defined as a surfer responding to the presented content according to the preferences of the site owner. Each predicted model can be associated with a key-performance indicator (KPI). Further, a predictive model can reflect the number of times the surfer requested the web page during the surfer'"'"'s visit.

Claims

1. A computer-implemented method, comprising:

generating, by a computing device, a plurality of predictive models for a web page of a website, wherein the web page includes a configuration defining one or more objects presented with the web page, wherein each object is associated with a predictive model, and wherein each predictive model is associated with one or more predictive model types;

determining the one more predictive model types that are associated with each predictive model in the plurality of predictive models;

determining a performance indicator that corresponds to each determined predictive model type, wherein performance indicators represent one or more benefits to a website;

selecting a predictive model out of the plurality of predictive models based on a performance indicator corresponding to a predictive model type of the selected predictive model; and

determining a configuration of the web page using the selected predictive model.

2. The method of claim 1, wherein selecting the predictive model out of the plurality of predictive models is further based on a readiness of the selected predictive model.

3. The method of claim 1, wherein performance indicators include a rate of clicking on an object, a rate of converting the object to a purchase, or a revenue rate generated by presenting a configuration of the web page.

4. The method of claim 1, further comprising:

gradually migrating over time from using a first type of predictive model associated with a first performance indicator to using a second type of predictive model associated with a second performance indicator.

5. The method of claim 1, further comprising:

receiving a request for the web page;

retrieving predictive information related to the request;

converting the predictive information into one or more predictive factors for an object presented with the web page;

defining a value for each of the one or more predictive factors; and

generating a predictive model for the object using the one or more predictive factors.

6. The method of claim 5, wherein the predictive information includes behavioral information and information associated with the request for the web page.

7. The method of claim 6, wherein the behavioral information includes a time of one or more previous visits in a domain that includes the requested web page, a time that the web page was last requested, a number of visits in the domain, a number of times that the object has been presented, and a number of times that the object has been selected.

8. The method of claim 7, wherein the behavioral information is from a single resource.

9. The method of claim 7, wherein the behavioral information is stored in a cookie.

10. The method of claim 6, wherein the information associated with the request for the web page includes a receipt time of the request, a uniform resource locator key associated with the request, an internet protocol address, or a type of browser application.

11. A system, comprising:

a processor; and

a non-transitory computer-readable storage medium containing instructions configured to cause the processor to perform operations including;

generating a plurality of predictive models for a web page of a website, wherein the web page includes a configuration defining one or more objects presented with the web page, wherein each object is associated with a predictive model, and wherein each predictive model is associated with one or more predictive model types;

determining the one more predictive model types that are associated with each predictive model in the plurality of predictive models;

determining a performance indicator that corresponds to each determined predictive model type, wherein performance indicators represent one or more benefits to a website;

selecting a predictive model out of the plurality of predictive models based on a performance indicator corresponding to a predictive model type of the selected predictive model; and

determining a configuration of the web page using the selected predictive model.

12. The system of claim 11, wherein selecting the predictive model out of the plurality of predictive models is further based on a readiness of the selected predictive model.

13. The system of claim 11, wherein performance indicators include a rate of clicking on an object, a rate of converting the object to a purchase, or a revenue rate generated by presenting a configuration of the web page.

14. The system of claim 11, further comprising instructions configured to cause the processor to perform operations including:

gradually migrating over time from using a first type of predictive model associated with a first performance indicator to using a second type of predictive model associated with a second performance indicator.

15. The system of claim 11, further comprising instructions configured to cause the processor to perform operations including:

receiving a request for the web page;

retrieving predictive information related to the request;

converting the predictive information into one or more predictive factors for an object presented with the web page;

defining a value for each of the one or more predictive factors; and

generating a predictive model for the object using the one or more predictive factors.

16. The system of claim 15, wherein the predictive information includes behavioral information and information associated with the request for the web page.

17. The system of claim 16, wherein the behavioral information includes a time of one or more previous visits in a domain that includes the requested web page, a time that the web page was last requested, a number of visits in the domain, a number of times that the object has been presented, and a number of times that the object has been selected.

18. The system of claim 17, wherein the behavioral information is from a single resource.

19. The system of claim 17, wherein the behavioral information is stored in a cookie.

20. The system of claim 16, wherein the information associated with the request for the web page includes a receipt time of the request, a uniform resource locator key associated with the request, an internet protocol address, or a type of browser application.

21. A computer-program product, tangibly embodied in a non-transitory machine-readable medium, including instructions configured to cause a data processing apparatus to:

generate a plurality of predictive models for a web page of a website, wherein the web page includes a configuration defining one or more objects presented with the web page, wherein each object is associated with a predictive model, and wherein each predictive model is associated with one or more predictive model types;

determine the one more predictive model types that are associated with each predictive model in the plurality of predictive models;

determine a performance indicator that corresponds to each determined predictive model type, wherein performance indicators represent one or more benefits to a website;

select a predictive model out of the plurality of predictive models based on a performance indicator corresponding to a predictive model type of the selected predictive model; and

determine a configuration of the web page using the selected predictive model.

22. The computer-program product of claim 21, wherein selecting the predictive model out of the plurality of predictive models is further based on a readiness of the selected predictive model.

23. The computer-program product of claim 21, wherein performance indicators include a rate of clicking on an object, a rate of converting the object to a purchase, or a revenue rate generated by presenting a configuration of the web page.

24. The computer-program product of claim 21, further comprising instructions configured to cause the data processing apparatus to:

gradually migrate over time from using a first type of predictive model associated with a first performance indicator to using a second type of predictive model associated with a second performance indicator.

25. The computer-program product of claim 21, further comprising instructions configured to cause the data processing apparatus to:

receive a request for the web page;

retrieve predictive information related to the request;

convert the predictive information into one or more predictive factors for an object presented with the web page;

define a value for each of the one or more predictive factors; and

generate a predictive model for the object using the one or more predictive factors.

26. The computer-program product of claim 25, wherein the predictive information includes behavioral information and information associated with the request for the web page.

27. The method of claim 26, wherein the behavioral information includes a time of one or more previous visits in a domain that includes the requested web page, a time that the web page was last requested, a number of visits in the domain, a number of times that the object has been presented, and a number of times that the object has been selected.

28. The computer-program product of claim 27, wherein the behavioral information is from a single resource.

29. The computer-program product of claim 27, wherein the behavioral information is stored in a cookie.

30. The computer-program product of claim 26, wherein the information associated with the request for the web page includes a receipt time of the request, a uniform resource locator key associated with the request, an internet protocol address, or a type of browser application.